Genetic Programming: v. 1 On the Programming of Computers by Means of Natural Selection (inbunden)
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Inbunden (Hardback)
Antal sidor
MIT Press
Koza, John R
v. 1 On the Programming of Computers by Means of Natural Selection
265 x 190 x 50 mm
1580 g
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Genetic Programming: v. 1 On the Programming of Computers by Means of Natural Selection

Inbunden, Engelska, 1993-02-01


Genetic programming may be more powerful than neural networks and other machine learning techniques, able to solve problems in a wider range of disciplines. In this ground-breaking book, John Koza shows how this remarkable paradigm works and provides substantial empirical evidence that solutions to a great variety of problems from many different fields can be found by genetically breeding populations of computer programs. Genetic Programming contains a great many worked examples and includes a sample computer code that will allow readers to run their own programs.In getting computers to solve problems without being explicitly programmed, Koza stresses two points: that seemingly different problems from a variety of fields can be reformulated as problems of program induction, and that the recently developed genetic programming paradigm provides a way to search the space of possible computer programs for a highly fit individual computer program to solve the problems of program induction. Good programs are found by evolving them in a computer against a fitness measure instead of by sitting down and writing them.
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The research reported in this book is a tour de force. For the first time, since the idea was bandied about in the '40s and early '50s, we have a non-trivial, nontailored set of examples of automatic programming." John Holland

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Övrig information

John R. Koza is Consulting Associate Professor in the Computer Science Department at Stanford University.


Pervasiveness of the problem of program induction; introduction to genetic algorithms; the representation problem for genetic algorithms; overview of genetic programming; detailed description of genetic programming; four introductory examples of genetic programming; amount of processing required to solve a problem; non-randomness of genetic programming; symbolic regression - error-driven evolution; control - cost-driven evolution; evolution of emergent behaviour; evolution of subsumption; entropy-driven evolution; evolution of strategy; co-evolution; evolution of classification; iteration, recursion, and setting; evolution of constrained syntactic structures; evolution of building blocks; evolution of hierarchies of building blocks; parallelization of genetic programming; ruggedness of genetic programming; extraneous variables and functions; operational issues; review of genetic programming; comparison with other paradigms; spontaneous emergence of self-replicating and evolutionarily self-improving computer programs. Appendices: computer implementation; problem-specific part of simple LISP code; kernel of the simple LISP code; embellishments to the simple LISP code; streamlined version of EVAL; editor for simplifying S-expressions; testing the simple LISP code; time-saving techniques; list of special symbols; list of special functions.